1. Field of the Invention
The invention relates to an image restoration method and an image processing apparatus using the same.
2. Description of Related Art
The visibility of the image captured in inclement weather conditions, such as fog, sand, and mist, will be degraded, since the suspended particles absorb and scatter specific spectrums of light between the observed objects and the camera. These images having degraded visibility lose contrast and color fidelity and are called as haze images. Accordingly, these haze images may directly reduce the performance quality of many systems, such as outdoor object recognition systems, obstacle detection systems, video surveillance systems, intelligent transportation systems and so on. That means, haze removal technique (or dehazing technique) is highly desired in plenty of systems using the haze image or video to achieve different functions. Hence, numerous visibility restoration approaches have been proposed to restore the visibility of the haze images in order to improve system performance in the inclement weather conditions. These visibility restoration approaches can further be divided into three categories, which are additional-information approaches, multiple-image approaches and single-image approaches.
Additional-information approaches restore hazy images by using given scene depth information obtain from either an additional operation or use interaction, such as through user operation to control camera positions and via a given approximate 3D geometrical model. However, these additional-information approaches are not well-suited for real-world application due to limitations placed on the acquisition of scene depth information by unknown geography information and additional user operation. Besides, multiple-image approaches adopt at least two images of the same scene, which are captured by using specific hardware, such as a rotating polarizing filter, to effectively construct the scene depth information and further achieve visibility restoration of incoming hazy images. Unfortunately, the use of these multiple-image approaches usually requires either excessive hardware expense or special devices.
Recently, single-image approaches have been focused on to restore the visibility of hazy images. Single-image approaches are based on either strong assumptions or robust priors, by which haze thickness is estimated by using only a single image. For instance, since a haze-free image possesses evident contrasts compared with a hazy image, the visibility of a hazy image may be restored by maximizing local contrast of the hazy image. However, the images restored by maximizing local contrast often feature serious artifacts along depth edges. Besides, there is another method to restore the visibility of hazy images by estimating the albedo of a scene and inferring the transmission medium, and it is accomplished via the assumption that the transmission medium and the surface shading are locally uncorrelated. Nevertheless, this method usually fails at restoration when the incoming images contain heavy haze formation.
Further, with the exception of sky regions, an outdoor haze-free image features at least one spectrum in the RGB color channels that exhibits a very low intensity value within patches of the image. Inspired by this observation, dark channel prior (DCP) method has been proposed by which to effectively estimate the thickness of haze information and further restore scene radiance. Until now, the DCP method has received the most attention due to its relative success in restoring visibility in hazy images. Based on the dark channel prior method, some improved DCP methods by which to achieve visibility restoration in degraded images captured in poor weather conditions are proposed.
One of the improved DCP methods explores the characteristics of both the dark channel prior and the Multi-Scale Retinex techniques to restore visibility in hazy images. Another one of the improved DCP methods restores visibility through the use of the refined transmission map which can be acquired by using the fast bilateral filter. The other one of the improved DCP methods restores visibility by adopting the three-time subtraction procedure to approximate the minimum value in the dark channel of hazy images. As mentioned above, these DCP-based techniques focus on refining the transmission map and can effectively produce high-quality, haze-free images without generation of any block effects for hazy image captured in foggy weather conditions.
However, these DCP-based techniques mentioned above usually fail in restoring the visibility of images whose haze is the result of capture during sandstorm conditions. This is due to the hindrance of restoration ability by color cast problems and insufficient estimation of haze thickness. Therefore, how to effectively restore haze images captured in any condition has been an important goal to be achieved by the persons skilled in the field.